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1/21/2004Stephen Scott, Univ. of Nebraska2 What is Machine Learning? Building machines that automatically learn from experience – Important research goal of artificial intelligence (Very) small sampling of applications: – Data mining programs that learn to detect fraudulent credit card transactions – Programs that learn to filter spam email – Autonomous vehicles that learn to drive on public highways

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1/21/2004Stephen Scott, Univ. of Nebraska3 What is Learning? Many different answers, depending on the field you’re considering and whom you ask – AI vs. psychology vs. education vs. neurobiology vs. …

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1/21/2004Stephen Scott, Univ. of Nebraska8 Again, what is Machine Learning? Given several labeled examples of a concept – E.g. trucks vs. non-trucks Examples are described by features – E.g. number-of-wheels (integer), relative-height (height divided by width), hauls-cargo (yes/no) A machine learning algorithm uses these examples to create a hypothesis that will predict the label of new (previously unseen) examples Similar to a very simplified form of human learning Hypotheses can take on many forms

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1/21/2004Stephen Scott, Univ. of Nebraska11 Other Hypothesis Types Nearest neighbor – Compare new (unlabeled) examples to ones you’ve memorized Support vector machines – A new way of looking at artificial neural networks Bagging and boosting – Performance enhancers for learning algorithms Many more – See your local machine learning instructor for details

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1/21/2004Stephen Scott, Univ. of Nebraska16 Machine Learning vs. Expert Systems (cont’d) ES: Expertise might not be comprehensive, e.g. physician might not have seen some types of cases ML: Automatic, objective, and data-driven – Though it is only as good as the available data

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1/21/2004Stephen Scott, Univ. of Nebraska18 More Detailed Example: Content-Based Image Retrieval Given database of hundreds of thousands of images How can users easily find what they want? One idea: Users query database by image content – E.g. “give me images with a waterfall”

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1/21/2004Stephen Scott, Univ. of Nebraska19 Content-Based Image Retrieval (cont’d) One approach: Someone annotates each image with text on its content – Tedious, terminology ambiguous, maybe subjective Better approach: Query by example – Users give examples of images they want – Program determines what’s common among them and finds more like them

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1/21/2004Stephen Scott, Univ. of Nebraska21 User’s feedback then labels the new images, which are used as more training examples, yielding a new hypothesis, and more images are retrieved Content-Based Image Retrieval (cont’d)

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1/21/2004Stephen Scott, Univ. of Nebraska22 How Does the System Work? For each pixel in the image, extract its color + the colors of its neighbors These colors (and their relative positions in the image) are the features the learner uses (replacing e.g. number- of-wheels) A learning algorithm takes examples of what the user wants, produces a hypothesis of what’s common among them, and uses it to label new images

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1/21/2004Stephen Scott, Univ. of Nebraska26 Conclusions ML started as a field that was mainly for research purposes, with a few niche applications Now applications are very widespread ML is able to automatically find patterns in data that humans cannot However, still very far from emulating human intelligence! – Each artificial learner is task-specific